Who Uses eConsult? Investigating Physician Characteristics Associated with Usage (and Nonusage)
Bibliographic record
Abstract
BACKGROUND: The Champlain BASE™ eConsult Service was developed in a Local Health Integration Network (LHIN) in Ontario, Canada in 2010 to reduce wait times and improve access to specialist care. The service allows primary care providers to receive advice from specialists via a secure electronic platform without necessarily requiring a face-to-face consultation. INTRODUCTION: As of 2015, over half of the LHIN's family physicians were registered and trained to use the service. However, 24% of registrants never went on to submit a case. The purpose of this study is to examine the demographic characteristics associated with usage. MATERIALS AND METHODS: Usage data for the pool of physicians registered between January 1, 2011 and September 30, 2015 were linked to physician characteristics retrieved from the College of Physicians and Surgeons of Ontario database. Probit regressions were estimated to determine characteristics associated with usage. RESULTS: Neither sex, being an international medical school graduate-documented predictors of electronic medical records adoption-nor proximity to specialists were found to explain usage. Only length of time in practice was found to be predictive. Being out of medical school an additional 10 years was estimated to decrease the probability of ever using eConsult by five percentage points (p < 0.01). CONCLUSION: Lower use by veteran physicians may reflect their lower need for services like eConsult given their well-established specialist networks, or their greater confidence in practicing medicine. Future work should explore the reasons and barriers for not registering, or not using eConsult, with an aim toward increasing the appropriate use of this cost-effective and innovative service.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".